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Record W2571280113 · doi:10.1177/1329878x1415100121

Hindsight in 2020? New Zealand's ‘Wait and See’ Approach to Mobile Broadband Regulation

2014· article· en· W2571280113 on OpenAlexaboutno aff
Michael Daubs

Bibliographic record

VenueMedia International Australia · 2014
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsHindsight biasMobile broadbandCompetition (biology)TelecommunicationsBroadbandContext (archaeology)Common value auctionMobile serviceBusinessMobile telephonySpectrum managementEconomicsService (business)MarketingComputer scienceMobile radioWireless

Abstract

fetched live from OpenAlex

New Zealand's Ministry of Business, Innovation and Employment's Review of the Telecommunications Act 2001, released in 2013, highlighted an increased demand for mobile broadband service, particularly in relation to the 700 MHz spectrum auction of 14 January 2014 – space ideal for next-generation 4G or Long Term Evolution (LTE) mobile services. The government seemingly adopted a ‘wait and see’ approach to mobile broadband regulation, however, delaying its development until 2020 when there will be ‘a clearer sense of the impact of new networks and technology’. One can look to Canada to see the need for robust mobile broadband policies. Like New Zealand, Canada has relied primarily upon spectrum auctions to stimulate market competition. The spectrum auction frameworks used there, however, have done little to promote market competition. Applying the lessons learned from Canada to a New Zealand context, this article argues for a more assertive regulatory framework sooner rather than later.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.017
Scholarly communication0.0130.008
Open science0.0020.003
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.254
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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